1. Introduction to the Course FREE CHAPTER

• SAS Studio/SAS OnDemand for Academics – Register, Upload, Run Code
• WPS – How to Find, Install, Register for, Use, and Upload Datasets
• Common Problems with Install/Upload (Watch for Best Course Experience!)

2. Importing

• Import .txt
• Import .csv
• Import .XLSX

3. SAS Syntax, Data Step Versus Proc Step, SAS Compared to R/Python

• Data Step Versus Proc Step
• SAS Syntax
• Manually Creating Data with R, Python, and SAS

4. Working with Data

• Data Set Options
• What If Your Data Is Separated by a Dot or Something Else? (Delimiters)
• Reading Data Instream in Data Step (Typing Data Right into Coding Area)
• Reading DATES in Data
• Creating Variables/Calculations
• More on Creating New Variables
• Automatic Variables
• Filtering Observations (So Only Some Data Shows Up)
• Intuition for If-Then/Else and Do, Do-While, Do-Until
• If-Then Conditional Logic
• DO Iterative Loop and Variations (DO WHILE, DO Until)
• More on DO Group Processing (Without Index/Counter Variable)
• More on the WHERE Expression/Statement
• Sorting Observations (PROC SORT and BY Statements)
• Merging Two Datasets
• Using SET Statement to Merge
• Data Reduction and Cleaning Your Data
• LENGTH Statement
• Creating a Counting (Enumeration) Variable

5. Back to Importing

• Importing SPSS File with SAS Language

6. Input Types and Informats + User-Defined Formats

• List Input
• Column Input
• Formatted Input and Informats
• User-Defined Formats

7. Arrays

• Arrays 1 (Recoding Variables)
• Arrays 2 (Constructing New Variables)

8. SAS Functions

• Understanding SAS Functions
• RAND Function (Producing a Sample with Distribution of Your Choice)
• LENGTH, LENGTHN, LENGTHC Functions (Are You Working with a Large Dataset?)
• TRIM Function (Want to Get Rid of Trailing Blanks?)
• COMPRESS Function (Remove Characters from String, and All Types of Blanks)
• Input and Put Functions
• CATX Function
• SCAN Function
• Coalesce Function
• Verify Function
• Substr Function

9. Advanced Techniques – Flexibilities and Efficiency

• Flexible Programming 1 - Combining multiple raw data files vertically

10. Visual Representation of Data

• Scatter Plot
• Bar Graph

11. Statistical Analysis

• T-Test Independent Samples Overview (Example)
• Doing an Independent Samples T-Test Analysis
• Chi-Square Independence Overview (Example)
• Doing a Chi-Square (Independent Groups) Analysis

12. Statistical Analysis – Part 2 (Linear and Multiple Regression)

• Refresh Your Memory - Regression Edition
• Performing the Linear Regression
• Performing Multiple Regression

13. Case Studies

• Case Study (HealthCare Case Study) - Part 1
• Case Study (HealthCare Case Study) - Part 2
• Congrats on Finishing Part 1: Data Step

14. SQL Fundamentals

• SQL Syntax
• WHERE Clause
• SELECT Statement and Columns
• CASE Logic
• Summary Functions

15. SAS SQL and Joining

• How to Perform an Inner Join
• How to Join Three Tables
• How to Perform a Left/Right Join
• How to Perform a Full Join

16. Working with Tables Using SAS SQL

• How to Create a Table Using SAS SQL
• Altering Columns (Add, Modify, Delete, Add Values to Column)
• Inserting Rows with a Query and Set Statement

17. Practical Application of SAS SQL

• How to Compare Tables with SAS SQL
• Finding Duplicate Observations
• Customize the Way You Sort
• How to Update a Table Under Certain Conditions with SAS SQL

18. Fundamentals of Utilizing SAS Indexes

• Intro to Indexes/Indices
• Should You Use an Index?
• Types of Indices
• Index Options
• Testing with Large Datasets
• Selecting Variable(s) for Your Index
• PROC Datasets and WHERE Expression
• BY Statement (Sorting Variables, While Exploiting Your Index)
• Handling Common Tasks with an Indexed Dataset
• Updating the Master Dataset with New Variables or Observations

19. Macro Facility Fundamentals

• Types of Macro Variables
• Don't Lose Track of Your Macro Variables
• Macro Variable Assignment Rules
• Masking Special Characters
• Macro Functions (%Index and %Upcase)
• Macro Functions 2 (%Scan)
• Creating a Macro Variable (Helps You Modify Data Easier)
• Macro Programs Introduction
• Creating a Macro Example 1 (Greater Flexibility and Useful for Repetitive Coding)
• Creating a Macro Example 2 (Unique Sales Reports for Different Days)
• Creating a Macro Example 3 (Calculating Average Sales for Multiple Years)
• Debugging Options
• Storing Macros (External)
• Brainstorming for Logistic Macro Case Study
• Logistic Macro, Case Study – Part 1
• Logistic Macro, Case Study – Part 2

20. Introduction to SAS Predictive Modeling Using Logistic Regression

• Business Applications of Predictive Modeling
• Analytics Challenges
• The Major Steps in Predictive Modeling
• Intuitive Understanding of Logistic Regression

21. SAS Model – Predictive Modeling, Understanding the Problem and the Data

• Problem Statement/Hypothesis Generation
• Data Audit
• Univariate Analysis
• Bivariate Analysis
• Important Housekeeping

22. SAS Predictive Modeling, Prepare the Input Variables

• Sources, Patterns, and Mechanisms of Missing Data
• Evaluating Missing Data Patterns with SAS
• 3 Phase Multiple Imputation Process Using SAS
• Considering the Output from PROC MI
• Oversampling and Adjusting for Oversampling
• Categorical Inputs
• Variable Clustering
• Multicollinearity
• Subset Selection
• Parameter Estimates

23. SAS Predictive Modeling, Evaluation Metrics

• ROC Curve
• Scoring Validation Dataset Using Code
• Decile Calibration Plot
• Feature Engineering

24. Extra Content

• Top Five SAS Certification Questions Answered